Machine learning is field of study that gives computers the ability to learn without being explicitly programmed. Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions, rather than following strictly static program instructions.
Machine learning is closely related to and often overlaps with computational statistics; a discipline which also focuses in prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms is in-feasible. Example applications include spam filtering,optical character recognition (OCR), search engines and computer vision.
Challenge for Machine learning experts is not only to know these techniques but choose best approach to address a specific business problem using combination of these techniques. As every company’s problem is unique and it’s impossible to cover every possible situation, we encourage you to contact us to receive specific advice on studying your processes.
Why Machine Learning: With brick and mortar stores, data was purchase data of a customer at a particular time on a particular day. With e-Commerce and m-Commerce industry, data has exploded with visits of potential customers on the website visiting website few times, leaving ordering process at certain stages, accepting recommended items on spot, visiting recommended websites, reading reviews and changing decisions,… This huge amount of continuous data need specialized approaches like machine learning to automatically study patterns in retail and website visit data.
Learning From Data: Based on study of customer behavior coming on the website, appropriate products can be displayed. It can also be used to send appropriate discount coupons to the customers, run appropriate advertising campaign and study impact of advertising campaign on customers. It can also be used to evaluate at what state of ordering process, customer backs out and potential reasons to give customers better experience. Whether customers feel more comfortable with calls, chat, emails, social networks, we can study their preferences and maintain market lead?
Warehouse Layout: In Supply chain industry, one can study pattern of different items ordered and keep different items in warehouse or fulfillment center in cost effective ways.
Pattern Recognition: Machine learning techniques can be used to study important patterns in the data like delay, demand, weather, traffic etc. Whether there are few variables or hundreds of them, scientific application of machine learning approaches can be very useful. Such approaches can be used in vessel planning, railways, trucking, airline, car rental, automotive and any other transportation channel.
Using Results: This information can be used to maintain competitive edge, start new transportation lane and adjust schedule to be more cost effective in your approach.
Pharmaceutical Industry: Machine learning techniques can be used to identify pattern in disease symptoms w.r.t. time and demographics. It can study effectiveness of difference medicines as well as similarity and differences in symptoms of different diseases. This information can be used to cluster different kind of patients in different groups and use these clusters to devise effective treatment plan.
Data Consolidation: Health care industry is a sensitive industry and it may not be easy to get all the data from medical providers and they themselves may not have patient information at a level easy for consolidation. To study medical history of a patient using multiple medical facility, machine learning techniques can be employed to consolidate data by studying patterns of similarity.
Pattern Recognition: Machine learning techniques can be used to study demand pattern in call, chat and email data of different customer groups for different kind of services. It can also be used for clustering different groups of data with similarities and be useful in identifying cause for anomalies and filling missing data.
Strategic Planning: This information can be used to strategically plan number of agents with different qualification, their cross training and remain competitive in today’s cost effective customer centric market. Automatic pattern recognition can help alert companies in early stages to adjust their strategy. It can help reallocate agents and reduce waiting time without increasing cost.